Use of Predictive Models to Determine Transplant Eligibility.

Purpose Of Review: This paper summarizes predictive models developed to determine transplant eligibility over the past 5 years, focusing on application of novel data sources and methodologic approaches.

Recent Findings: The contemporary body of research employing predictive models to inform transplant eligibility mainly relies on pre- or post-transplant patient survival. No studies have sought to assimilate all features collected during the transplant evaluation process to produce a composite prediction of post-transplant success or failure.

Development of Multiservice Machine Learning Models to Predict Postsurgical Length of Stay and Discharge Disposition at the Time of Case Posting.

Objective: Develop machine learning (ML) models to predict postsurgical length of stay (LOS) and discharge disposition (DD) for multiple services with only the data available at the time of case posting.

Background: Surgeries are scheduled largely based on operating room resource availability with little attention to downstream resource availability such as inpatient bed availability and the care needs after hospitalization. Predicting postsurgical LOS and DD at the time of case posting could support resource allocation and earlier discharge planning.

Ambulatory Surgery Ensemble: Predicting Adult and Pediatric Same-Day Surgery Cases Across Specialties.

Objective: To develop an ensemble model using case-posting data to predict which patients could be discharged on the day of surgery.

Background: Few models have predicted which surgeries are appropriate for day cases. Increasing the ratio of ambulatory surgeries can decrease costs and inpatient bed utilization while improving resource utilization.

Development of a natural language processing algorithm to extract social determinants of health from clinician notes.

Disparities in access to the organ transplant waitlist are well-documented, but research into modifiable factors has been limited due to a lack of access to organized prewaitlisting data. This study aimed to develop a natural language processing (NLP) algorithm to extract social determinants of health (SDOH) from free-text notes and quantify the association of SDOH with access to the transplant waitlist. We collected 261 802 clinician notes from 11 111 adults referred for kidney or liver transplants between 2016 and 2022 at the Duke University Health System.

Prognostic Implications of Codon-Specific Mutations in Localized and Advanced Stages of Pancreatic Cancer.

Introduction: While mutations represent the primary oncogenic driver in pancreatic ductal adenocarcinoma (PDAC), the association between codon-specific alterations and patient outcomes remains poorly elucidated, largely due to a lack of datasets coupling genomic profiling with rich clinical annotations across disease stages.

Patients And Methods: We utilized AACR's GENIE Biopharma Consortium Pancreas v1.2 dataset to test the associating of codon-specific mutations with clinicogenomic features and patient outcomes in PDAC patients diagnosed with localized (stages I-III) and advanced disease (stage IV).

A machine learning prediction model for total shoulder arthroplasty procedure duration: an evaluation of surgeon, patient, and shoulder-specific factors.

Background: Operating room efficiency is of paramount importance for scheduling, cost efficiency, and to allow for the high operating volume required to address the growing demand for arthroplasty. The purpose of this study was to develop a machine learning predictive model for total shoulder arthroplasty (TSA) procedure duration and to identify factors which are predictive of a prolonged procedure.

Methods: A retrospective review was undertaken of all TSA between 2013 and 2021 in a large academic institution.

Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation.

Objective: To implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations.

Background: The operating room is one of the most expensive resources in a health system, estimated to cost $22 to $133 per minute and generate about 40% of hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the operating room and other resources.

Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study.

The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke Protected Analytics Computing Environment (PACE) to facilitate data-sharing and big data analytics across the US and Singapore.

Social determinants of health data in solid organ transplantation: National data sources and future directions.

Health equity research in transplantation has largely relied on national data sources, yet the availability of social determinants of health (SDOH) data varies widely among these sources. We sought to characterize the extent to which national data sources contain SDOH data applicable to end-stage organ disease (ESOD) and transplant patients. We reviewed 10 active national data sources based in the United States.

CPT to RVU conversion improves model performance in the prediction of surgical case length.

Methods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT-RVU combined.

Exploiting codon usage identifies intensity-specific modifiers of Ras/MAPK signaling in vivo.

Signal transduction pathways are intricately fine-tuned to accomplish diverse biological processes. An example is the conserved Ras/mitogen-activated-protein-kinase (MAPK) pathway, which exhibits context-dependent signaling output dynamics and regulation. Here, by altering codon usage as a novel platform to control signaling output, we screened the Drosophila genome for modifiers specific to either weak or strong Ras-driven eye phenotypes.

Wnt signaling suppresses MAPK-driven proliferation of intestinal stem cells.

Intestinal homeostasis depends on a slowly proliferating stem cell compartment in crypt cells, followed by rapid proliferation of committed progenitor cells in the transit amplifying (TA) compartment. The balance between proliferation and differentiation in intestinal stem cells (ISCs) is regulated by Wnt/β-catenin signaling, although the mechanism remains unclear. We previously targeted PORCN, an enzyme essential for all Wnt secretion, and demonstrated that stromal production of Wnts was required for intestinal homeostasis.

Engineering plasmonic nanorod arrays for colon cancer marker detection.

Engineering plasmonic nanomaterials or nanostructures towards ultrasensitive biosensing for disease markers or pathogens is of high importance. Here we demonstrate a systematic approach to tailor effective plasmonic nanorod arrays by combining both comprehensive numerical discrete dipole approximations (DDA) simulation and transmission spectroscopy experiments. The results indicate that 200×50 nm nanorod arrays with 300×500 nm period provide the highest figure of merit (FOM) of 2.

Stroma provides an intestinal stem cell niche in the absence of epithelial Wnts.

Wnt/β-catenin signaling supports intestinal homeostasis by regulating proliferation in the crypt. Multiple Wnts are expressed in Paneth cells as well as other intestinal epithelial and stromal cells. Ex vivo, Wnts secreted by Paneth cells can support intestinal stem cells when Wnt signaling is enhanced with supplemental R-Spondin 1 (RSPO1).

Early detection of biomolecular changes in disrupted porcine cartilage using polarized Raman spectroscopy.

We evaluate the feasibility of applying polarized Raman spectroscopy in probing the early biochemical compositions and orientation changes in impacted porcine cartilage explants. We divide 100 fresh tibial cartilage explants into four groups: control (unimpacted) and 3 groups of single impact at 15, 20, and 25 MPa. Each group is examined for biochemical changes using Raman microscopy, cell viability changes using confocal fluorescence microscopy, and histological changes using the modified Mankin score.